Human Gait Data Research Articles

身体動作における個人特徴の抽出とその分類に関する検証

In recent years, there has been a growing needs of the sensing system to acquire the position and attitude of the people in the field in the medical and sports. As future, it will be possible to quantitatively determine the individual differences and proficiency in body motion. The purpose of this paper is to perform feature extraction using Singular Value Decomposition for human gait data.

Biologically-inspired push recovery capable bipedal locomotion modeling through hybrid automata

The earlier developed two stage hybrid automata is not a perfect representation of human walk as it is a combination of discrete and continuous phases and the whole human GAIT has 8 stages. Our major contribution is eight stage hybrid automata for large push recovery and various dynamic parameter studies for stable walk model. We have developed a controller to verify different stage of human locomotion by using OpenSim data for model 3DGaitModel2354 and lower extremity data. We verified the hybrid automata model using the real human GAIT data for normal person. We identify the importance of the human lower extremity for locomotion and push recovery from large perturbation. The novelty of research work is to model the bipedal locomotion as a re-usable component based framework. Our original contribution lies in the fact that we have tried to view it from a software engineering perspective.

A novel approach for representing and generalising periodic gaits

SUMMARYOur goal is to introduce a more appropriate method of representing, generalising and comparing gaits; particularly, walking gait. Human walking gaits are a result of complex, interdependent factors that include variations resulting from embodiments, environment and tasks, making techniques that use average template frameworks suboptimal for systematic analysis or corrective interventions. The proposed work aims to devise methodologies for being able to represent gaits and gait transitions such that optimal policies that eliminate the inter-personal variations from tasks and embodiments may be recovered. Our approach is built upon (i) work in the domain of nullspace policy recovery and (ii) previous work in generalisation for point-to-point movements. The problem is formalised using a walking-phase model, and the nullspace learning method is used to generalise a consistent policy from multiple observations with rich variations. Once recovered, the underlying policies (mapped to different gait phases) can serve as reference guideline to quantify and identify pathological gaits while being robust against interpersonal and task variations. To validate our methods, we have demonstrated robustness of our method with simulated sagittal two-link gait data with multiple ground truth constraints and policies. Pathological gait identification was then tested on real-world human gait data with induced gait abnormality, with the proposed method showing significant robustness to variations in speed and embodiment compared to template-based methods. Future work will extend this to kinetic features and higher dimensional features.

Wearable acceleration sensor application in unilateral trans-tibial amputation prostheses

The availability of human walking gait data collected from the wearable acceleration sensors for trajectory control of an active artificial ankle joint in the unilateral trans-tibial prosthesis was investigated in this study. It is observed that the collected acceleration data can be used in the rulebased control of the prosthetic leg. A portable microprocessor-based data acquisition system, and data transfer module were designed for capturing the acceleration signals during walking. Flexionextension angle pattern of ankle joint was determined from acceleration signals of two tri-axial wearable accelerometers placed on the shank and foot segments. This pattern was utilized for control of the active artificial ankle joint in the trans-tibial prosthesis. This approach may have the potential of contributing the development of better prostheses.

Model selection for the extraction of movement primitives

A wide range of blind source separation methods have been used in motor control research for the extraction of movement primitives from EMG and kinematic data. Popular examples are principal component analysis (PCA), independent component analysis (ICA), anechoic demixing, and the time-varying synergy model (d'Avella and Tresch, 2002). However, choosing the parameters of these models, or indeed choosing the type of model, is often done in a heuristic fashion, driven by result expectations as much as by the data. We propose an objective criterion which allows to select the model type, number of primitives and the temporal smoothness prior. Our approach is based on a Laplace approximation to the posterior distribution of the parameters of a given blind source separation model, re-formulated as a Bayesian generative model. We first validate our criterion on ground truth data, showing that it performs at least as good as traditional model selection criteria [Bayesian information criterion, BIC (Schwarz, 1978) and the Akaike Information Criterion (AIC) (Akaike, 1974)]. Then, we analyze human gait data, finding that an anechoic mixture model with a temporal smoothness constraint on the sources can best account for the data.

DEVELOPMENT OF MOTION CAPTURE SYSTEM USING DUAL VIDEO CAMERAS FOR THE GAIT DESIGN OF A BIPED ROBOT

This paper, describes the development of a motion capture system with novel features for biped robots. In general, motion capture is effectively utilized in the field of computer animation. In the field of humanoid robotics, the number of studies attempting to design human-like gaits by using expensive optical motion capture systems is increasing. The optical motion capture systems used in these studies have involved a large number of cameras because such systems use small-sized ball markers; hence the position accuracy of the markers and the system calibration are very significant. However, since the human walking gait is a simple periodic motion rather than a complex motion, we have developed a specialized motion capture system for this study using dual video cameras and large band-type markers without high-level system calibration in order to capture the human walking gait. In addition to its lower complexity, the proposed capture method requires only a low-cost system and has high space efficiency. An image processing algorithm is also proposed for deriving the human gait data. Finally, we verify the reliability and accuracy of our system by comparing a zero moment point (ZMP) trajectory calculated by the motion captured data with a ZMP trajectory measured by foot force sensors.

Optimised torque trajectory for humanoid robot based on human gait data

This paper presents an optimised torque trajectory for biped robot based on human walking gait data. A mathematical dynamic model of leg of a biped robot having active hip, knee and ankle joint is considered here for walking performance. The torque trajectory obtained from mathematical calculation of dynamics of human leg is optimised on the basis of a cost function to minimise the energy dissipation. Torque trajectory of human leg is obtained using simulation software BRG LifeMOD. The three torque trajectories namely simulated, calculated and optimised are compared, and the optimised trajectory obtained in this paper can be directly used as input to the control theoretic applications of walking humanoid robot. Therefore, the proposed method can be used to generate an optimised torque trajectory.

Rhythmic Dynamics and Synchronization via Dimensionality Reduction: Application to Human Gait

Reliable characterization of locomotor dynamics of human walking is vital to understanding the neuromuscular control of human locomotion and disease diagnosis. However, the inherent oscillation and ubiquity of noise in such non-strictly periodic signals pose great challenges to current methodologies. To this end, we exploit the state-of-the-art technology in pattern recognition and, specifically, dimensionality reduction techniques, and propose to reconstruct and characterize the dynamics accurately on the cycle scale of the signal. This is achieved by deriving a low-dimensional representation of the cycles through global optimization, which effectively preserves the topology of the cycles that are embedded in a high-dimensional Euclidian space. Our approach demonstrates a clear advantage in capturing the intrinsic dynamics and probing the subtle synchronization patterns from uni/bivariate oscillatory signals over traditional methods. Application to human gait data for healthy subjects and diabetics reveals a significant difference in the dynamics of ankle movements and ankle-knee coordination, but not in knee movements. These results indicate that the impaired sensory feedback from the feet due to diabetes does not influence the knee movement in general, and that normal human walking is not critically dependent on the feedback from the peripheral nervous system.

Data series embedding and scale invariant statistics

Data sequences acquired from bio-systems such as human gait data, heart rate interbeat data, or DNA sequences exhibit complex dynamics that is frequently described by a long-memory or power–law decay of autocorrelation function. One way of characterizing that dynamics is through scale invariant statistics or “fractal-like” behavior. For quantifying scale invariant parameters of physiological signals several methods have been proposed. Among them the most common are detrended fluctuation analysis, sample mean variance analyses, power spectral density analysis, R/S analysis, and recently in the realm of the multifractal approach, wavelet analysis. In this paper it is demonstrated that embedding the time series data in the high-dimensional pseudo-phase space reveals scale invariant statistics in the simple fashion. The procedure is applied on different stride interval data sets from human gait measurements time series (Physio-Bank data library). Results show that introduced mapping adequately separates long-memory from random behavior. Smaller gait data sets were analyzed and scale-free trends for limited scale intervals were successfully detected. The method was verified on artificially produced time series with known scaling behavior and with the varying content of noise. The possibility for the method to falsely detect long-range dependence in the artificially generated short range dependence series was investigated.

The Design and Modeling of Multi-axis Knee Artificial Leg

To provide an ideal test-base for the development of an intelligent bionic leg, a new pattern humanoid robot – biped robot with heterogeneous legs (BRHL) was designed. To simulate a normal human gait, a multi-axis knee mechanism was included into the artificial leg design that was different from the artificial leg of a common biped robot. The conception and research purposed for the BRHL was developed. Based on human bioscience, the bionic design of multi-axis knee artificial leg was analyzed and a virtual prototype was made. The kinematics model and dynamics model were deduced in detail. Based on human normal gait data, a simulation of dynamics model was carried. An optimized mechanical design of multi-axis knee artificial leg is discussed and a simulation was done. The optimal mechanism parameters for the multi-axis knee artificial leg and a BRHL prototype were given. This research indicated that a multi-axis knee artificial leg can simulate a human leg.

Doppler spectrogram analysis of human gait via iterative adaptive approach

Doppler spectrogram analysis of the human gait is a useful tool for discriminating various microDoppler tracks due to the movements of different body parts. A data-dependent algorithm, namely the short-time iterative adaptive approach, is used to obtain a more accurate spectrogram than the one provided by the conventional short-time Fourier transform-based approaches. The performance of the approaches is demonstrated and contrasted using both simulated and measured human gait data.

Gait Feature Subset Selection by Mutual Information

Feature subset selection is an important preprocessing step for pattern recognition, to discard irrelevant and redundant information, as well as to identify the most important attributes. In this paper, we investigate a computationally efficient solution to select the most important features for gait recognition. The specific technique applied is based on mutual information (MI), which evaluates the statistical dependence between two random variables and has an established relation with the Bayes classification error. Extending our earlier research, we show that a sequential selection method based on MI can provide an effective solution for high-dimensional human gait data. To assess the performance of the approach, experiments are carried out based on a 73-dimensional model-based gait features set and on a 64 by 64 pixels model-free gait symmetry map on the Southampton HiD Gait database. The experimental results confirm the effectiveness of the method, removing about 50% of the model-based features and 95% of the symmetry map's pixels without significant loss in recognition capability, which outperforms correlation and analysis-of-variance-based methods.

Low-Dimensional Sagittal Plane Model of Normal Human Walking

This paper applies a robotics-inspired approach to derive a low-dimensional forward-dynamic hybrid model of human walking in the sagittal plane. The low-dimensional model is derived as a subdynamic of a higher-dimensional anthropomorphic hybrid model. The hybrid model is composed of models for single support (SS) and double support (DS), with the transition from SS to DS modeled by a rigid impact to account for the impact at heel-contact. The transition from DS to SS occurs in a continuous manner. Existing gait data are used to specify, via parametrization, the low-dimensional model that is developed. The primary result is a one-degree-of-freedom model that is an exact subdynamic of the higher-dimensional anthropomorphic model and describes the dynamics of walking. The stability properties of the model are evaluated using the method of Poincare. The low-dimensional model is validated using the measured human gait data. The validation demonstrates the observed stability of the measured gait.

Kinematical modeling and optimal design of a biped robot joint parallel linkage

This paper shows the design and analysis of a parallel three-dimensional linkage, conceived to work as the ankle and hip joints of an anthropometric biped robot. This kind of mechanism architecture provides low-weight, highly stable assemblies, and allows the use of actuator synergies. On the other hand, the mechanical transmission ratio is not usually favorable, and a non-linear kinematic model has to be derived and solved. The mechanism proposed here is driven by two rotational servo-actuators, and allows the joint to follow a specified angular trajectory determined by the gait pattern. Namely, the joint linkage can generate dorsi/plantar flexion and inversion/eversion of the ankle, and hip flexion/extension and adduction/abduction movements. Several approaches to the direct and inverse kinematical modeling of the linkage are presented and compared, regarding their accuracy and computational cost, where the last performance parameter is closely related to on-line computer implementing of the controller. Strategies to fit current gait angular amplitudes into the linkage workspace, as well as singularity analysis, are discussed. An optimization method was applied to find some geometrical design parameters of the linkage that minimizes a cost function. This function is the mean transmission ratio between the motor inputs and the joint output torques over a predefined dominion. The minimization is constrained to a minimum workspace area value and to minimum and maximum values of the design parameters. Several design solutions were generated. The chosen was one where the workspace is compatible to the gait amplitude requirements and that exhibits the lowest cost function. A biped robot using the linkage geometry designed in this paper has been built and tested with real human gait data acquired in a gait lab.

Exotendons for assistance of human locomotion.

BackgroundPowered robotic exoskeletons for assistance of human locomotion are currently under development for military and medical applications. The energy requirements for such devices are excessive, and this has become a major obstacle for practical applications. Legged locomotion in many animals, however, is very energy efficient. We propose that poly-articular elastic mechanisms are a major contributor to the economy of locomotion in such specialized animals. Consequently, it should be possible to design unpowered assistive devices that make effective use of similar mechanisms.MethodsA passive assistive technology is presented, based on long elastic cords attached to an exoskeleton and guided by pulleys placed at the joints. A general optimization procedure is described for finding the best geometrical arrangement of such "exotendons" for assisting a specific movement. Optimality is defined either as minimal residual joint moment or as minimal residual joint power. Four specific exotendon systems with increasing complexity are considered. Representative human gait data were used to optimize each of these four systems to achieve maximal assistance for normal walking.ResultsThe most complex exotendon system, with twelve pulleys per limb, was able to reduce the joint moments required for normal walking by 71% and joint power by 74%. A simpler system, with only three pulleys per limb, could reduce joint moments by 46% and joint power by 47%.ConclusionIt is concluded that unpowered passive elastic devices can substantially reduce the muscle forces and the metabolic energy needed for walking, without requiring a change in movement. When optimally designed, such devices may allow independent locomotion in patients with large deficits in muscle function.

3-Dimensional Sliding/Contact Computational Simulation of Total Hip Wear

Polyethylene wear in total hip replacements is a complex, multifactorial process. A tribologically grounded finite element formulation was developed to make quantitative estimates of polyethylene wear in total hip arthroplasty, incorporating the combined influences of contact stress, sliding distance, and a surface specific wear coefficient. For loading and sliding distance inputs taken directly from human gait data, the computational model showed a strong direct proportionality between femoral head size and volumetric wear rate. Other factors being equal, reducing the thickness of the polyethylene liner led to increases in the computed wear rates, but the effect was far less pronounced than the strong increases in wear rate that accompanied head size increases. Compared with human gait inputs, the load and sliding distance inputs for a 23° biaxial rocking hip simulator led to computed wear rates that were 1.7 times as large, and in which the direction of wear was near the cup apex rather than within the posterosuperolateral quadrant. In general, the finite element model's results emphasize the importance of articulation kinematics, especially sliding distance, in the complex process of polyethylene wear in total hip arthroplasty.